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  <h1>Source code for torch.nn.modules.transformer</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">.activation</span> <span class="kn">import</span> <span class="n">MultiheadAttention</span>
<span class="kn">from</span> <span class="nn">.container</span> <span class="kn">import</span> <span class="n">ModuleList</span>
<span class="kn">from</span> <span class="nn">..init</span> <span class="kn">import</span> <span class="n">xavier_uniform_</span>
<span class="kn">from</span> <span class="nn">.dropout</span> <span class="kn">import</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">.linear</span> <span class="kn">import</span> <span class="n">Linear</span>
<span class="kn">from</span> <span class="nn">.normalization</span> <span class="kn">import</span> <span class="n">LayerNorm</span>


<div class="viewcode-block" id="Transformer"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Transformer">[docs]</a><span class="k">class</span> <span class="nc">Transformer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A transformer model. User is able to modify the attributes as needed. The architecture</span>
<span class="sd">    is based on the paper &quot;Attention Is All You Need&quot;. Ashish Vaswani, Noam Shazeer,</span>
<span class="sd">    Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and</span>
<span class="sd">    Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information</span>
<span class="sd">    Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805)</span>
<span class="sd">    model with corresponding parameters.</span>

<span class="sd">    Args:</span>
<span class="sd">        d_model: the number of expected features in the encoder/decoder inputs (default=512).</span>
<span class="sd">        nhead: the number of heads in the multiheadattention models (default=8).</span>
<span class="sd">        num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).</span>
<span class="sd">        num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).</span>
<span class="sd">        dim_feedforward: the dimension of the feedforward network model (default=2048).</span>
<span class="sd">        dropout: the dropout value (default=0.1).</span>
<span class="sd">        activation: the activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).</span>
<span class="sd">        custom_encoder: custom encoder (default=None).</span>
<span class="sd">        custom_decoder: custom decoder (default=None).</span>

<span class="sd">    Examples::</span>
<span class="sd">        &gt;&gt;&gt; transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)</span>
<span class="sd">        &gt;&gt;&gt; src = torch.rand((10, 32, 512))</span>
<span class="sd">        &gt;&gt;&gt; tgt = torch.rand((20, 32, 512))</span>
<span class="sd">        &gt;&gt;&gt; out = transformer_model(src, tgt)</span>

<span class="sd">    Note: A full example to apply nn.Transformer module for the word language model is available in</span>
<span class="sd">    https://github.com/pytorch/examples/tree/master/word_language_model</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">nhead</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_encoder_layers</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
                 <span class="n">num_decoder_layers</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
                 <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="n">custom_encoder</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">custom_decoder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Transformer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="k">if</span> <span class="n">custom_encoder</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">custom_encoder</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">encoder_layer</span> <span class="o">=</span> <span class="n">TransformerEncoderLayer</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="p">,</span> <span class="n">dropout</span><span class="p">,</span> <span class="n">activation</span><span class="p">)</span>
            <span class="n">encoder_norm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">TransformerEncoder</span><span class="p">(</span><span class="n">encoder_layer</span><span class="p">,</span> <span class="n">num_encoder_layers</span><span class="p">,</span> <span class="n">encoder_norm</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">custom_decoder</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">custom_decoder</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">decoder_layer</span> <span class="o">=</span> <span class="n">TransformerDecoderLayer</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="p">,</span> <span class="n">dropout</span><span class="p">,</span> <span class="n">activation</span><span class="p">)</span>
            <span class="n">decoder_norm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">TransformerDecoder</span><span class="p">(</span><span class="n">decoder_layer</span><span class="p">,</span> <span class="n">num_decoder_layers</span><span class="p">,</span> <span class="n">decoder_norm</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_parameters</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">d_model</span> <span class="o">=</span> <span class="n">d_model</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nhead</span> <span class="o">=</span> <span class="n">nhead</span>

<div class="viewcode-block" id="Transformer.forward"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Transformer.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="n">src_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tgt_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="n">memory_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="n">tgt_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">memory_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -&gt; Tensor  # noqa</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Take in and process masked source/target sequences.</span>

<span class="sd">        Args:</span>
<span class="sd">            src: the sequence to the encoder (required).</span>
<span class="sd">            tgt: the sequence to the decoder (required).</span>
<span class="sd">            src_mask: the additive mask for the src sequence (optional).</span>
<span class="sd">            tgt_mask: the additive mask for the tgt sequence (optional).</span>
<span class="sd">            memory_mask: the additive mask for the encoder output (optional).</span>
<span class="sd">            src_key_padding_mask: the ByteTensor mask for src keys per batch (optional).</span>
<span class="sd">            tgt_key_padding_mask: the ByteTensor mask for tgt keys per batch (optional).</span>
<span class="sd">            memory_key_padding_mask: the ByteTensor mask for memory keys per batch (optional).</span>

<span class="sd">        Shape:</span>
<span class="sd">            - src: :math:`(S, N, E)`.</span>
<span class="sd">            - tgt: :math:`(T, N, E)`.</span>
<span class="sd">            - src_mask: :math:`(S, S)`.</span>
<span class="sd">            - tgt_mask: :math:`(T, T)`.</span>
<span class="sd">            - memory_mask: :math:`(T, S)`.</span>
<span class="sd">            - src_key_padding_mask: :math:`(N, S)`.</span>
<span class="sd">            - tgt_key_padding_mask: :math:`(N, T)`.</span>
<span class="sd">            - memory_key_padding_mask: :math:`(N, S)`.</span>

<span class="sd">            Note: [src/tgt/memory]_mask should be filled with</span>
<span class="sd">            float(&#39;-inf&#39;) for the masked positions and float(0.0) else. These masks</span>
<span class="sd">            ensure that predictions for position i depend only on the unmasked positions</span>
<span class="sd">            j and are applied identically for each sequence in a batch.</span>
<span class="sd">            [src/tgt/memory]_key_padding_mask should be a ByteTensor where True values are positions</span>
<span class="sd">            that should be masked with float(&#39;-inf&#39;) and False values will be unchanged.</span>
<span class="sd">            This mask ensures that no information will be taken from position i if</span>
<span class="sd">            it is masked, and has a separate mask for each sequence in a batch.</span>

<span class="sd">            - output: :math:`(T, N, E)`.</span>

<span class="sd">            Note: Due to the multi-head attention architecture in the transformer model,</span>
<span class="sd">            the output sequence length of a transformer is same as the input sequence</span>
<span class="sd">            (i.e. target) length of the decode.</span>

<span class="sd">            where S is the source sequence length, T is the target sequence length, N is the</span>
<span class="sd">            batch size, E is the feature number</span>

<span class="sd">        Examples:</span>
<span class="sd">            &gt;&gt;&gt; output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">if</span> <span class="n">src</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="n">tgt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;the batch number of src and tgt must be equal&quot;</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">src</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_model</span> <span class="ow">or</span> <span class="n">tgt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_model</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;the feature number of src and tgt must be equal to d_model&quot;</span><span class="p">)</span>

        <span class="n">memory</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">src_mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="o">=</span><span class="n">src_key_padding_mask</span><span class="p">)</span>
        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">tgt</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">tgt_mask</span><span class="o">=</span><span class="n">tgt_mask</span><span class="p">,</span> <span class="n">memory_mask</span><span class="o">=</span><span class="n">memory_mask</span><span class="p">,</span>
                              <span class="n">tgt_key_padding_mask</span><span class="o">=</span><span class="n">tgt_key_padding_mask</span><span class="p">,</span>
                              <span class="n">memory_key_padding_mask</span><span class="o">=</span><span class="n">memory_key_padding_mask</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">output</span></div>

<div class="viewcode-block" id="Transformer.generate_square_subsequent_mask"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Transformer.generate_square_subsequent_mask">[docs]</a>    <span class="k">def</span> <span class="nf">generate_square_subsequent_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sz</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Generate a square mask for the sequence. The masked positions are filled with float(&#39;-inf&#39;).</span>
<span class="sd">            Unmasked positions are filled with float(0.0).</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">sz</span><span class="p">,</span> <span class="n">sz</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">masked_fill</span><span class="p">(</span><span class="n">mask</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;-inf&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">masked_fill</span><span class="p">(</span><span class="n">mask</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">mask</span></div>

    <span class="k">def</span> <span class="nf">_reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Initiate parameters in the transformer model.&quot;&quot;&quot;</span>

        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">p</span><span class="p">)</span></div>


<div class="viewcode-block" id="TransformerEncoder"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerEncoder">[docs]</a><span class="k">class</span> <span class="nc">TransformerEncoder</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;TransformerEncoder is a stack of N encoder layers</span>

<span class="sd">    Args:</span>
<span class="sd">        encoder_layer: an instance of the TransformerEncoderLayer() class (required).</span>
<span class="sd">        num_layers: the number of sub-encoder-layers in the encoder (required).</span>
<span class="sd">        norm: the layer normalization component (optional).</span>

<span class="sd">    Examples::</span>
<span class="sd">        &gt;&gt;&gt; encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)</span>
<span class="sd">        &gt;&gt;&gt; transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)</span>
<span class="sd">        &gt;&gt;&gt; src = torch.rand(10, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; out = transformer_encoder(src)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">__constants__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoder_layer</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerEncoder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">_get_clones</span><span class="p">(</span><span class="n">encoder_layer</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>

<div class="viewcode-block" id="TransformerEncoder.forward"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerEncoder.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor], Optional[Tensor]) -&gt; Tensor</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pass the input through the encoder layers in turn.</span>

<span class="sd">        Args:</span>
<span class="sd">            src: the sequence to the encoder (required).</span>
<span class="sd">            mask: the mask for the src sequence (optional).</span>
<span class="sd">            src_key_padding_mask: the mask for the src keys per batch (optional).</span>

<span class="sd">        Shape:</span>
<span class="sd">            see the docs in Transformer class.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">src</span>

        <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">mod</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">src_mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="o">=</span><span class="n">src_key_padding_mask</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">output</span></div></div>


<div class="viewcode-block" id="TransformerDecoder"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerDecoder">[docs]</a><span class="k">class</span> <span class="nc">TransformerDecoder</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;TransformerDecoder is a stack of N decoder layers</span>

<span class="sd">    Args:</span>
<span class="sd">        decoder_layer: an instance of the TransformerDecoderLayer() class (required).</span>
<span class="sd">        num_layers: the number of sub-decoder-layers in the decoder (required).</span>
<span class="sd">        norm: the layer normalization component (optional).</span>

<span class="sd">    Examples::</span>
<span class="sd">        &gt;&gt;&gt; decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)</span>
<span class="sd">        &gt;&gt;&gt; transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)</span>
<span class="sd">        &gt;&gt;&gt; memory = torch.rand(10, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; tgt = torch.rand(20, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; out = transformer_decoder(tgt, memory)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">__constants__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;norm&#39;</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decoder_layer</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerDecoder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">_get_clones</span><span class="p">(</span><span class="n">decoder_layer</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>

<div class="viewcode-block" id="TransformerDecoder.forward"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerDecoder.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">tgt_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="n">memory_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tgt_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="n">memory_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -&gt; Tensor</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pass the inputs (and mask) through the decoder layer in turn.</span>

<span class="sd">        Args:</span>
<span class="sd">            tgt: the sequence to the decoder (required).</span>
<span class="sd">            memory: the sequence from the last layer of the encoder (required).</span>
<span class="sd">            tgt_mask: the mask for the tgt sequence (optional).</span>
<span class="sd">            memory_mask: the mask for the memory sequence (optional).</span>
<span class="sd">            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).</span>
<span class="sd">            memory_key_padding_mask: the mask for the memory keys per batch (optional).</span>

<span class="sd">        Shape:</span>
<span class="sd">            see the docs in Transformer class.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">tgt</span>

        <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">mod</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">tgt_mask</span><span class="o">=</span><span class="n">tgt_mask</span><span class="p">,</span>
                         <span class="n">memory_mask</span><span class="o">=</span><span class="n">memory_mask</span><span class="p">,</span>
                         <span class="n">tgt_key_padding_mask</span><span class="o">=</span><span class="n">tgt_key_padding_mask</span><span class="p">,</span>
                         <span class="n">memory_key_padding_mask</span><span class="o">=</span><span class="n">memory_key_padding_mask</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">output</span></div></div>

<div class="viewcode-block" id="TransformerEncoderLayer"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerEncoderLayer">[docs]</a><span class="k">class</span> <span class="nc">TransformerEncoderLayer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;TransformerEncoderLayer is made up of self-attn and feedforward network.</span>
<span class="sd">    This standard encoder layer is based on the paper &quot;Attention Is All You Need&quot;.</span>
<span class="sd">    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,</span>
<span class="sd">    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in</span>
<span class="sd">    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement</span>
<span class="sd">    in a different way during application.</span>

<span class="sd">    Args:</span>
<span class="sd">        d_model: the number of expected features in the input (required).</span>
<span class="sd">        nhead: the number of heads in the multiheadattention models (required).</span>
<span class="sd">        dim_feedforward: the dimension of the feedforward network model (default=2048).</span>
<span class="sd">        dropout: the dropout value (default=0.1).</span>
<span class="sd">        activation: the activation function of intermediate layer, relu or gelu (default=relu).</span>

<span class="sd">    Examples::</span>
<span class="sd">        &gt;&gt;&gt; encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)</span>
<span class="sd">        &gt;&gt;&gt; src = torch.rand(10, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; out = encoder_layer(src)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerEncoderLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span> <span class="o">=</span> <span class="n">MultiheadAttention</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
        <span class="c1"># Implementation of Feedforward model</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">dim_feedforward</span><span class="p">,</span> <span class="n">d_model</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">_get_activation_fn</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
        <span class="k">if</span> <span class="s1">&#39;activation&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state</span><span class="p">:</span>
            <span class="n">state</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerEncoderLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__setstate__</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>

<div class="viewcode-block" id="TransformerEncoderLayer.forward"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerEncoderLayer.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">src_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">src_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor], Optional[Tensor]) -&gt; Tensor</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pass the input through the encoder layer.</span>

<span class="sd">        Args:</span>
<span class="sd">            src: the sequence to the encoder layer (required).</span>
<span class="sd">            src_mask: the mask for the src sequence (optional).</span>
<span class="sd">            src_key_padding_mask: the mask for the src keys per batch (optional).</span>

<span class="sd">        Shape:</span>
<span class="sd">            see the docs in Transformer class.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">src2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">attn_mask</span><span class="o">=</span><span class="n">src_mask</span><span class="p">,</span>
                              <span class="n">key_padding_mask</span><span class="o">=</span><span class="n">src_key_padding_mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">src</span> <span class="o">=</span> <span class="n">src</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span><span class="p">(</span><span class="n">src2</span><span class="p">)</span>
        <span class="n">src</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
        <span class="n">src2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">src</span><span class="p">))))</span>
        <span class="n">src</span> <span class="o">=</span> <span class="n">src</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span><span class="p">(</span><span class="n">src2</span><span class="p">)</span>
        <span class="n">src</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">src</span></div></div>


<div class="viewcode-block" id="TransformerDecoderLayer"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerDecoderLayer">[docs]</a><span class="k">class</span> <span class="nc">TransformerDecoderLayer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.</span>
<span class="sd">    This standard decoder layer is based on the paper &quot;Attention Is All You Need&quot;.</span>
<span class="sd">    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,</span>
<span class="sd">    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in</span>
<span class="sd">    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement</span>
<span class="sd">    in a different way during application.</span>

<span class="sd">    Args:</span>
<span class="sd">        d_model: the number of expected features in the input (required).</span>
<span class="sd">        nhead: the number of heads in the multiheadattention models (required).</span>
<span class="sd">        dim_feedforward: the dimension of the feedforward network model (default=2048).</span>
<span class="sd">        dropout: the dropout value (default=0.1).</span>
<span class="sd">        activation: the activation function of intermediate layer, relu or gelu (default=relu).</span>

<span class="sd">    Examples::</span>
<span class="sd">        &gt;&gt;&gt; decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)</span>
<span class="sd">        &gt;&gt;&gt; memory = torch.rand(10, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; tgt = torch.rand(20, 32, 512)</span>
<span class="sd">        &gt;&gt;&gt; out = decoder_layer(tgt, memory)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerDecoderLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span> <span class="o">=</span> <span class="n">MultiheadAttention</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">multihead_attn</span> <span class="o">=</span> <span class="n">MultiheadAttention</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
        <span class="c1"># Implementation of Feedforward model</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">dim_feedforward</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="n">dim_feedforward</span><span class="p">,</span> <span class="n">d_model</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm3</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout3</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">_get_activation_fn</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
        <span class="k">if</span> <span class="s1">&#39;activation&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state</span><span class="p">:</span>
            <span class="n">state</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">TransformerDecoderLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__setstate__</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>

<div class="viewcode-block" id="TransformerDecoderLayer.forward"><a class="viewcode-back" href="../../../../nn.html#torch.nn.TransformerDecoderLayer.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">tgt_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">memory_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="n">tgt_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">memory_key_padding_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -&gt; Tensor</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pass the inputs (and mask) through the decoder layer.</span>

<span class="sd">        Args:</span>
<span class="sd">            tgt: the sequence to the decoder layer (required).</span>
<span class="sd">            memory: the sequence from the last layer of the encoder (required).</span>
<span class="sd">            tgt_mask: the mask for the tgt sequence (optional).</span>
<span class="sd">            memory_mask: the mask for the memory sequence (optional).</span>
<span class="sd">            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).</span>
<span class="sd">            memory_key_padding_mask: the mask for the memory keys per batch (optional).</span>

<span class="sd">        Shape:</span>
<span class="sd">            see the docs in Transformer class.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">tgt2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span><span class="p">(</span><span class="n">tgt</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="n">attn_mask</span><span class="o">=</span><span class="n">tgt_mask</span><span class="p">,</span>
                              <span class="n">key_padding_mask</span><span class="o">=</span><span class="n">tgt_key_padding_mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="n">tgt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span><span class="p">(</span><span class="n">tgt2</span><span class="p">)</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">tgt</span><span class="p">)</span>
        <span class="n">tgt2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multihead_attn</span><span class="p">(</span><span class="n">tgt</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">memory</span><span class="p">,</span> <span class="n">attn_mask</span><span class="o">=</span><span class="n">memory_mask</span><span class="p">,</span>
                                   <span class="n">key_padding_mask</span><span class="o">=</span><span class="n">memory_key_padding_mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="n">tgt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span><span class="p">(</span><span class="n">tgt2</span><span class="p">)</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">tgt</span><span class="p">)</span>
        <span class="n">tgt2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">tgt</span><span class="p">))))</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="n">tgt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout3</span><span class="p">(</span><span class="n">tgt2</span><span class="p">)</span>
        <span class="n">tgt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm3</span><span class="p">(</span><span class="n">tgt</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">tgt</span></div></div>


<span class="k">def</span> <span class="nf">_get_clones</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ModuleList</span><span class="p">([</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">module</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">)])</span>


<span class="k">def</span> <span class="nf">_get_activation_fn</span><span class="p">(</span><span class="n">activation</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;relu&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span>
    <span class="k">elif</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;gelu&quot;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">gelu</span>

    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;activation should be relu/gelu, not </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">activation</span><span class="p">))</span>
</pre></div>

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